DQRs

DQRs stands for Data Quality Requirements, a set of criteria that data must satisfy to support its intended use within an organization. They define the quality standards for data assets and guide activities such as data profiling, cleansing, integration, and governance.

Typically, DQRs cover dimensions such as accuracy, completeness, timeliness, consistency, validity, and uniqueness. Depending on the context, they may also address accessibility, provenance, lineage, and business relevance. DQRs should be aligned with business objectives, regulatory obligations, and risk tolerance.

Development of DQRs involves collaboration among data owners, data stewards, and business users. They are documented in data governance artifacts such as data dictionaries, policy documents, and data quality catalogs. Each requirement is paired with metrics and thresholds to enable automated checks as well as human reviews.

Implementation of DQRs is achieved through data processing pipelines, databases, APIs, and data warehouses by applying validation rules, constraints, profiling, and monitoring dashboards. They help determine remediation priorities and guide data quality improvement initiatives.

Evaluation of data quality uses dashboards and scorecards to assess DQR compliance during ingestion, migration, and change management. Over time, DQRs may be refined in response to evolving business needs or newly identified quality issues.

Other uses of the acronym DQRs exist in different domains, so readers should rely on context to determine the intended meaning. In data management, however, DQRs most commonly refer to data quality requirements.